Senior Staff/Principal Engineer, Foundation Model Compute Infra

Apple • Cupertino, CA

2d

About The Position

As a Senior Staff/Principal Engineer on the Foundation Model Compute Infrastructure team, you will architect, build, and operate large-scale infrastructure for foundation model training, fine-tuning, evaluation, and inference. You will address distributed scheduling, accelerator utilization, cluster management, fault tolerance, and large-scale job orchestration. You will work with cloud and hardware partners to bring new TPU generations into production and build tools and services that let researchers launch, monitor, debug, and profile workloads without needing deep infrastructure expertise.

You will develop model inference and fine-tuning services and build secure, scalable sandbox infrastructure for AI agents, code execution, and reinforcement learning environments at massive scale. Working closely with foundation model researchers and engineers, you will improve performance, reliability, scalability, and developer productivity. This is a hands-on technical leadership role involving writing, reviewing, and debugging production code, leading complex architectural decisions, and using modern AI-assisted development tools to accelerate engineering execution and raise the team's quality bar.

Requirements

• 8+ years of experience designing and building distributed systems or large-scale infrastructure, with demonstrated technical leadership.
• Strong hands-on experience with Kubernetes, cloud infrastructure, and operating production systems at scale.
• Proficiency in Python, Go, C++, or other programming languages used for infrastructure and distributed systems.
• Experience with accelerator infrastructure and machine learning training or inference workloads.
• Excellent written and verbal communication skills.
• Experience authoring technical design documents and driving collaboration across teams.

Nice To Haves

• Hands-on experience operating TPU, GPU, or other large-scale accelerator fleets.
• Experience scaling distributed training or inference workloads across thousands of accelerators.
• Familiarity with JAX, XLA, and distributed machine learning frameworks, including performance analysis and profiling.
• Experience developing LLM-based agents or AI-assisted developer and infrastructure operations tools.
• Deep knowledge of distributed scheduling, resource allocation, multi-tenant quota management, or cluster autoscaling.

Responsibilities

• Architect, build, and operate large-scale infrastructure for foundation model training, fine-tuning, evaluation, and inference.
• Address challenges in distributed scheduling, accelerator utilization, cluster management, fault tolerance, and large-scale job orchestration.
• Work with cloud and hardware partners to bring new TPU generations into production.
• Build tools and services that enable researchers to launch, monitor, debug, and profile workloads.
• Develop model inference and fine-tuning services.
• Build secure, scalable sandbox infrastructure for AI agents, code execution, and reinforcement learning environments at massive scale.
• Work with foundation model researchers and engineers to improve performance, reliability, scalability, and developer productivity.
• Write, review, and debug production code; lead complex architectural decisions; and use AI-assisted development tools to accelerate engineering and raise quality standards.
• Author technical design documents and drive collaboration across teams.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior
• Education Level: No Education Listed